Building Capacity for Data-Driven Urban Mobility

Keynote for the ITF-Eurostat Conference on Promoting Data-Driven Decision-Making

Professor Robin Lovelace

Institute for Transport Studies, University of Leeds

November 4, 2025

Introduction

  • Who am I? Professor of Transport Data Science at the University of Leeds.
  • My focus: Building open, reproducible, and policy-relevant transport planning tools.
  • Today’s topic: Building capacity for data-driven decision-making in urban mobility.
  • Why it matters for Southeast Europe: Supporting sustainable mobility goals and the path to EU collaboration.

The Challenge: The Data-to-Policy Gap

We have more data than ever before, but are we using it effectively?

Common Barriers:

  • Data Access: Data is often siloed, proprietary, or not in a usable format.
  • Skills & Tools: Lack of training and accessible tools to analyse the data.
  • Institutional Culture: Resistance to new methods and a disconnect between analysts and decision-makers.
  • Procurement: Over-reliance on ‘black box’ commercial solutions.

A Solution: Open & Reproducible Workflows

Openness builds trust and empowers collaboration.

  • Open Source: The code is free to use, modify, and share.
  • Open Data: Public data is accessible to everyone.
  • Open Methods: Methodologies are transparent and documented.
  • Open Access: Research and educational materials are freely available.
  • Community of Practice.

Example community of practice: QGIS

Why is QGIS so strong?

Aside: Geographic distribution of QGIS interest

Pillar 1: Open Data & Standards

Good analysis starts with good data.

  • Data Standards are Key:
    • GTFS for public transport schedules.
    • GBFS for shared micro-mobility (bikes, scooters).
    • OpenStreetMap for detailed street network data.
  • National Data Portals: A vital resource for official statistics and geographic data.
  • Example: Sourcing road network data from OpenStreetMap for a whole country.

Pillar 2: Open Source Tools

Powerful, free, and adaptable tools for transport analysis.

  • The R & Python Ecosystems: Mature, extensive libraries for data science, statistics, and visualisation.
  • Example (R): The stplanr package for transport planning and modelling.
  • Example (Python): geopandas and pandana for network analysis.
  • These tools can be adapted to local needs and data sources.

Pillar 3: Communities of Practice

Capacity building is not just about tools, it’s about people.

  • A ‘Community of Practice’ is a group of people who “share a concern or a passion for something they do and learn how to do it better as they interact regularly” (Wenger 1998).
  • Open source projects naturally foster these communities.
  • Examples: rOpenSci, rOpenSpain, QGIS user groups.
  • They provide support, share best practices, and drive innovation.
  • Key takeaway: To build capacity, invest in building communities.

Case Study 1: National-Scale Planning (UK)

From local data to national strategy with plan.activetravelengland.gov.uk.

  • Purpose: Prioritise billions of £ in investment for walking and cycling.
  • How it works:
    • Integrates dozens of open datasets (e.g., census, road safety, network data).
    • Uses a transparent, open-source model (pct R package).
    • Provides a web interface for planners across the country.
  • Impact: A consistent, evidence-based approach to national transport planning.

Active Travel England Tool: plan.activetravelengland.gov.uk

Case Study 2: Regional Planning (Scotland)

The Network Planning Tool (npt.scot).

  • Purpose: Help regional and local authorities design and prioritise active travel networks.
  • Features:
    • Open data and transparent methods.
    • Estimates of cycling potential and Level of Service.
    • Web UI for planners and the public.
  • Lesson: A powerful tool can be developed and deployed for a specific region, and can serve as a model for others.

Network Planning Tool for Scotland: npt.scot

Case Study 3: National OD data in Spain

  • The spanishoddata package provides access to Spain’s national origin-destination mobility survey.
  • It’s a community-driven project, adopted by rOpenSpain, ensuring long-term maintenance and quality.
  • The tool is used by researchers and public bodies, including the Spanish Ministry of Transport, for evidence-based planning.
  • This demonstrates a successful model of collaboration between government, academia, and the open-source community.

Mobility analysis with spanishoddata

Making it Happen in Southeast Europe

How can you apply these principles?

  1. Start with a Data Audit: What data do you have? What format is it in? What can be opened?
  2. Run a Pilot Project: Choose a specific problem (e.g., mapping cycling potential in one city) and solve it with open tools.
  3. Invest in Training: Build skills in R or Python for spatial data analysis within your statistical offices and transport authorities.
  4. Foster Regional Collaboration: Share data, code, and experiences with neighbouring countries facing similar challenges.

Future Directions: New Data & New Challenges

The landscape is always changing.

  • New Data Sources: Mobile phone data, GPS tracks, sensor data offer huge potential.
    • Companies can be paid or mandated to share anonymised data for public good, e.g. mobile network operators.
  • New Challenges:
    • Quality & Bias: Are these new datasets representative?
    • Privacy & Ethics: How do we use sensitive data responsibly?
    • Governance: Who owns the data and the insights?
  • New tools provide new ways to add value to existing datasets

Jittering Method for Privacy Preservation

Source: Kotov, Lovelace, and Vidal-Tortosa (2024)

Demo: Mapping Southeast Europe with Open Tools

Results created with the eurostat and giscoR packages to download and map official geospatial data for the region.

Southeast Europe Map

Thank you

  • Key message: Sustainable capacity is built on open data, open tools, and an open community.
  • Slides and code: github.com/robinlovelace/presentations
  • Contact: r.lovelace@leeds.ac.uk

References

Kotov, Egor, Robin Lovelace, and Eugeni Vidal-Tortosa. 2024. Spanishoddata. https://doi.org/10.32614/CRAN.package.spanishoddata.
Wenger, Etienne. 1998. Communities of Practice: Learning, Meaning, and Identity. Cambridge University Press.